Machines· 2026Q2
Generative AI vs. Traditional Machine Learning for Energy-Efficient and Circular Manufacturing in Industry 5.0: A Life-Cycle-Based Framework for Sustainable Manufacturing
- 1citations
- Q2SCImago
- 2026year
Short summary
A new Industry 5.0 framework integrates Generative AI (GenAI) and traditional Machine Learning (ML) to optimize manufacturing for energy efficiency and circularity, showing potential for reduced resource consumption and improved eco-design.
AI-generated from the title and abstract; the full text is not read.
Key points
- Industry 5.0 framework integrates GenAI and traditional ML for sustainable manufacturing.
- GenAI generates eco-design solutions and sustainable process configurations.
- Traditional ML predicts real-time energy consumption, emissions, and material losses.
- A life cycle assessment (LCA) is proposed to estimate environmental impact.
- Preliminary analyses suggest potential for reduced resource use and improved circularity.
AI-generated from the title and abstract; the full text is not read.
Abstract
This study proposes an integrated Industry 5.0 framework that compares and combines generative artificial intelligence (GenAI) with traditional machine learning (ML) to enhance the sustainability of manufacturing systems. This framework supports energy-efficient process optimisation, waste minimisation, material recycling and environmentally friendly production planning through data-driven decision-making. GenAI is more commonly used to generate sustainable process configurations and alternative eco-design solutions, whilst traditional ML models predict energy consumption, emissions and material losses in real time. It is proposed that a life cycle assessment (LCA) be carried out to estimate the environmental impact at all stages of production. Preliminary analyses indicate significant potential for reducing resource consumption, improving the circular economy, and supporting more sustainable and resilient manufacturing ecosystems and supply chains.
The authors' abstract, as published at the source. Machines, 2026 · DOI ↗
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Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering